Tootfinder

Breaking Anonymity at Scale: Re-identifying the Trajectories of 100K Real Users in Japan
Abhishek Kumar Mishra, Mathieu Cunche, Heber H. Arcolezi
https://arxiv.org/abs/2506.05611 …

Breaking Anonymity at Scale: Re-identifying the Trajectories of 100K Real Users in Japan
Mobility traces represent a critical class of personal data, often subjected to privacy-preserving transformations before public release. In this study, we analyze the anonymized Yjmob100k dataset, which captures the trajectories of 100,000 users in Japan, and demonstrate how existing anonymization techniques fail to protect their sensitive attributes. We leverage population density patterns, structural correlations, and temporal activity profiles to re-identify the dataset's real-world locatio…

The invisible agony of fish in Spain's fish farms #AnimalRights

The invisible agony of fish in Spain's fish farms
Spain is the leading aquaculture producer in the European Union, resulting in over 254 million fish bred and slaughtered annually in industrial fish farms. Yet these animals lack real protection, as revealed by our latest investigation.

Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation
Jinpeng Chen, Jianxiang He, Huan Li, Senzhang Wang, Yuan Cao, Kaimin Wei, Zhenye Yang, Ye Ji
https://arxiv.org/abs/2507.04623

Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation
Session-based Recommendation (SBR) aims to predict the next item a user will likely engage with, using their interaction sequence within an anonymous session. Existing SBR models often focus only on single-session information, ignoring inter-session relationships and valuable cross-session insights. Some methods try to include inter-session data but struggle with noise and irrelevant information, reducing performance. Additionally, most models rely on item ID co-occurrence and overlook rich sem…

Network Hexagons Under Attack: Secure Crowdsourcing of Geo-Referenced Data
Okemawo Obadofin, Joao Barros
https://arxiv.org/abs/2506.05601 https://

Network Hexagons Under Attack: Secure Crowdsourcing of Geo-Referenced Data
A critical requirement for modern-day Intelligent Transportation Systems (ITS) is the ability to collect geo-referenced data from connected vehicles and mobile devices in a safe, secure and anonymous way. The Nexagon protocol, which builds on the IETF Locator/ID Separation Protocol (LISP) and the Hierarchical Hexagonal Clustering (H3) geo-spatial indexing system, offers a promising framework for dynamic, privacy-preserving data aggregation. Seeking to address the critical security and privacy v…

Hello!
I have a happy favour to ask.
Last year, I had a US intern working with us and she now is looking for help that you- good reader of this account- could give.
(She needs academic survey participants)
Check out her flyer below, click the link, and please RT
http…

Research Study on Data Privacy and Collective Redress
A researcher at University College Dublin wants to conduct anonymous interviews to learn about why you would or would not participate in a legal action to rectify a data protection violation. This information can be used to help nonprofit organizations better represent you when organizations violate your data protection rights. Provide your email to receive more information. All personally identifiable information will be deleted following completion of the study.

Hello, won't you tell me your name?: Investigating Anonymity Abuse in IPFS
Christos Karapapas, Iakovos Pittaras, George C. Polyzos, Constantinos Patsakis
https://arxiv.org/abs/2506.04307

Hello, won't you tell me your name?: Investigating Anonymity Abuse in IPFS
The InterPlanetary File System~(IPFS) offers a decentralized approach to file storage and sharing, promising resilience and efficiency while also realizing the Web3 paradigm. Simultaneously, the offered anonymity raises significant questions about potential misuse. In this study, we explore methods that malicious actors can exploit IPFS to upload and disseminate harmful content while remaining anonymous. We evaluate the role of pinning services and public gateways, identifying their capabilitie…

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Yan Zhou, Bradley Malin, Murat Kantarcioglu
https://arxiv.org/abs/2506.01907 https://

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Privacy-preserving data publication, including synthetic data sharing, often experiences trade-offs between privacy and utility. Synthetic data is generally more effective than data anonymization in balancing this trade-off, however, not without its own challenges. Synthetic data produced by generative models trained on source data may inadvertently reveal information about outliers. Techniques specifically designed for preserving privacy, such as introducing noise to satisfy differential priva…

Rethinking Contrastive Learning in Session-based Recommendation
Xiaokun Zhang, Bo Xu, Fenglong Ma, Zhizheng Wang, Liang Yang, Hongfei Lin
https://arxiv.org/abs/2506.05044

Rethinking Contrastive Learning in Session-based Recommendation
Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailing in the task. However, we spot that existing contrastive learning based methods still suffer from three obstacles: (1) they overlook item-level sparsity and primarily focus on session-level sparsity; (2) they typically augment sessions using item IDs like crop, mask and reorder, failing to ensure the semantic consist…

An Algorithmic Pipeline for GDPR-Compliant Healthcare Data Anonymisation: Moving Toward Standardisation
Hamza Khan, Lore Menten, Liesbet M. Peeters
https://arxiv.org/abs/2506.02942

An Algorithmic Pipeline for GDPR-Compliant Healthcare Data Anonymisation: Moving Toward Standardisation
High-quality real-world data (RWD) is essential for healthcare but must be transformed to comply with the General Data Protection Regulation (GDPR). GDPRs broad definitions of quasi-identifiers (QIDs) and sensitive attributes (SAs) complicate implementation. We aim to standardise RWD anonymisation for GDPR compliance while preserving data utility by introducing an algorithmic method to identify QIDs and SAs and evaluate utility in anonymised datasets. We conducted a systematic literature review…

Towards Better Attribute Inference Vulnerability Measures
Paul Francis, David Wagner
https://arxiv.org/abs/2507.01710 https://arxiv.o…

Towards Better Attribute Inference Vulnerability Measures
The purpose of anonymizing structured data is to protect the privacy of individuals in the data while retaining the statistical properties of the data. An important class of attack on anonymized data is attribute inference, where an attacker infers the value of an unknown attribute of a target individual given knowledge of one or more known attributes. A major limitation of recent attribute inference measures is that they do not take recall into account, only precision. It is often the case tha…